Overview

Dataset statistics

Number of variables29
Number of observations134664
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.8 MiB
Average record size in memory232.0 B

Variable types

Numeric19
Categorical10

Alerts

demanda_energia is highly overall correlated with tmax-cab and 9 other fieldsHigh correlation
prec_cul_mm is highly overall correlated with prec_lmo_mmHigh correlation
prec_lmo_mm is highly overall correlated with prec_cul_mmHigh correlation
tmax-cab is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
tmax-cul is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
tmax-hmo is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
tmax-lmo is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
tmax-obr is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
tmin-cab is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
tmin-cul is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
tmin-hmo is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
tmin-lmo is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
tmin-obr is highly overall correlated with demanda_energia and 9 other fieldsHigh correlation
lunes_festivo is highly imbalanced (92.7%)Imbalance
martes_postfestivo is highly imbalanced (92.8%)Imbalance
semana_santa is highly imbalanced (89.0%)Imbalance
1_mayo is highly imbalanced (97.2%)Imbalance
10_mayo is highly imbalanced (97.5%)Imbalance
16_sep is highly imbalanced (97.2%)Imbalance
2_nov. is highly imbalanced (97.3%)Imbalance
pre-navidad_y_new_year is highly imbalanced (95.2%)Imbalance
navidad_y_new_year is highly imbalanced (95.1%)Imbalance
post-navidad_y_new_year is highly imbalanced (95.1%)Imbalance
prec_lmo_mm is highly skewed (γ1 = 22.00427544)Skewed
hora has 5611 (4.2%) zerosZeros
prec_hmo_mm has 117840 (87.5%) zerosZeros
prec_obr_mm has 119088 (88.4%) zerosZeros
prec_lmo_mm has 122808 (91.2%) zerosZeros
prec_cul_mm has 112416 (83.5%) zerosZeros

Reproduction

Analysis started2024-04-26 22:27:42.276605
Analysis finished2024-04-26 22:28:26.628395
Duration44.35 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

hora
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros5611
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:26.737639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222123
Coefficient of variation (CV)0.6019315
Kurtosis-1.2041741
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum1548636
Variance47.917022
MonotonicityNot monotonic
2024-04-26T15:28:26.825572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 5611
 
4.2%
1 5611
 
4.2%
22 5611
 
4.2%
21 5611
 
4.2%
20 5611
 
4.2%
19 5611
 
4.2%
18 5611
 
4.2%
17 5611
 
4.2%
16 5611
 
4.2%
15 5611
 
4.2%
Other values (14) 78554
58.3%
ValueCountFrequency (%)
0 5611
4.2%
1 5611
4.2%
2 5611
4.2%
3 5611
4.2%
4 5611
4.2%
5 5611
4.2%
6 5611
4.2%
7 5611
4.2%
8 5611
4.2%
9 5611
4.2%
ValueCountFrequency (%)
23 5611
4.2%
22 5611
4.2%
21 5611
4.2%
20 5611
4.2%
19 5611
4.2%
18 5611
4.2%
17 5611
4.2%
16 5611
4.2%
15 5611
4.2%
14 5611
4.2%

dia
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.706113
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:26.935027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8006052
Coefficient of variation (CV)0.56032993
Kurtosis-1.1947093
Mean15.706113
Median Absolute Deviation (MAD)8
Skewness0.010140287
Sum2115048
Variance77.450652
MonotonicityNot monotonic
2024-04-26T15:28:27.029175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 4440
 
3.3%
8 4440
 
3.3%
2 4440
 
3.3%
12 4440
 
3.3%
10 4440
 
3.3%
9 4440
 
3.3%
11 4440
 
3.3%
7 4440
 
3.3%
6 4440
 
3.3%
5 4440
 
3.3%
Other values (21) 90264
67.0%
ValueCountFrequency (%)
1 4440
3.3%
2 4440
3.3%
3 4440
3.3%
4 4440
3.3%
5 4440
3.3%
6 4440
3.3%
7 4440
3.3%
8 4440
3.3%
9 4440
3.3%
10 4440
3.3%
ValueCountFrequency (%)
31 2568
1.9%
30 4032
3.0%
29 4128
3.1%
28 4416
3.3%
27 4416
3.3%
26 4416
3.3%
25 4416
3.3%
24 4416
3.3%
23 4416
3.3%
22 4416
3.3%

mes
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4334343
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:27.107715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4618471
Coefficient of variation (CV)0.5381025
Kurtosis-1.2199472
Mean6.4334343
Median Absolute Deviation (MAD)3
Skewness0.026864682
Sum866352
Variance11.984386
MonotonicityNot monotonic
2024-04-26T15:28:27.201899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 11904
8.8%
3 11904
8.8%
4 11520
8.6%
5 11448
8.5%
7 11160
8.3%
8 11160
8.3%
10 11160
8.3%
12 11160
8.3%
2 10848
8.1%
6 10800
8.0%
Other values (2) 21600
16.0%
ValueCountFrequency (%)
1 11904
8.8%
2 10848
8.1%
3 11904
8.8%
4 11520
8.6%
5 11448
8.5%
6 10800
8.0%
7 11160
8.3%
8 11160
8.3%
9 10800
8.0%
10 11160
8.3%
ValueCountFrequency (%)
12 11160
8.3%
11 10800
8.0%
10 11160
8.3%
9 10800
8.0%
8 11160
8.3%
7 11160
8.3%
6 10800
8.0%
5 11448
8.5%
4 11520
8.6%
3 11904
8.8%

anio
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.1882
Minimum2007
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:27.296059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12010
median2014
Q32018
95-th percentile2021
Maximum2022
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.4383353
Coefficient of variation (CV)0.0022035355
Kurtosis-1.1952667
Mean2014.1882
Median Absolute Deviation (MAD)4
Skewness0.010480292
Sum2.7123864 × 108
Variance19.69882
MonotonicityIncreasing
2024-04-26T15:28:27.375205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2008 8784
 
6.5%
2012 8784
 
6.5%
2016 8784
 
6.5%
2020 8784
 
6.5%
2007 8760
 
6.5%
2009 8760
 
6.5%
2010 8760
 
6.5%
2011 8760
 
6.5%
2013 8760
 
6.5%
2014 8760
 
6.5%
Other values (6) 46968
34.9%
ValueCountFrequency (%)
2007 8760
6.5%
2008 8784
6.5%
2009 8760
6.5%
2010 8760
6.5%
2011 8760
6.5%
2012 8784
6.5%
2013 8760
6.5%
2014 8760
6.5%
2015 8760
6.5%
2016 8784
6.5%
ValueCountFrequency (%)
2022 3168
 
2.4%
2021 8760
6.5%
2020 8784
6.5%
2019 8760
6.5%
2018 8760
6.5%
2017 8760
6.5%
2016 8784
6.5%
2015 8760
6.5%
2014 8760
6.5%
2013 8760
6.5%

tmax-cab
Real number (ℝ)

HIGH CORRELATION 

Distinct909
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.84941
Minimum9
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:27.512059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile19.9
Q127
median33.4
Q339.2
95-th percentile44
Maximum50
Range41
Interquartile range (IQR)12.2

Descriptive statistics

Standard deviation7.8412686
Coefficient of variation (CV)0.23870348
Kurtosis-0.88136258
Mean32.84941
Median Absolute Deviation (MAD)6.4
Skewness-0.262304
Sum4423633
Variance61.485493
MonotonicityNot monotonic
2024-04-26T15:28:27.634653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 4560
 
3.4%
42 4392
 
3.3%
43 4248
 
3.2%
41 4224
 
3.1%
37 4080
 
3.0%
35 3816
 
2.8%
39 3744
 
2.8%
40 3744
 
2.8%
28 3720
 
2.8%
33 3672
 
2.7%
Other values (899) 94464
70.1%
ValueCountFrequency (%)
9 24
 
< 0.1%
11 24
 
< 0.1%
12 144
0.1%
13 144
0.1%
13.1 24
 
< 0.1%
13.2 24
 
< 0.1%
13.56 24
 
< 0.1%
13.59 24
 
< 0.1%
13.6 24
 
< 0.1%
14 264
0.2%
ValueCountFrequency (%)
50 48
 
< 0.1%
48.4 24
 
< 0.1%
48 144
0.1%
47.6 24
 
< 0.1%
47.4 24
 
< 0.1%
47.3 24
 
< 0.1%
47.28 24
 
< 0.1%
47 312
0.2%
46.8 48
 
< 0.1%
46.68 24
 
< 0.1%

tmax-hmo
Real number (ℝ)

HIGH CORRELATION 

Distinct883
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.468893
Minimum8
Maximum49.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:27.775678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile22
Q128.5
median34
Q339
95-th percentile43
Maximum49.1
Range41.1
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.6465622
Coefficient of variation (CV)0.19858924
Kurtosis-0.6694255
Mean33.468893
Median Absolute Deviation (MAD)5
Skewness-0.35969622
Sum4507055
Variance44.176789
MonotonicityNot monotonic
2024-04-26T15:28:28.533471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 5520
 
4.1%
39 5280
 
3.9%
36 5016
 
3.7%
38 4992
 
3.7%
35 4968
 
3.7%
40 4752
 
3.5%
33 4752
 
3.5%
41 4704
 
3.5%
32 4488
 
3.3%
34 4296
 
3.2%
Other values (873) 85896
63.8%
ValueCountFrequency (%)
8 24
 
< 0.1%
14.58 24
 
< 0.1%
15 24
 
< 0.1%
15.7 24
 
< 0.1%
16 216
0.2%
16.4 24
 
< 0.1%
16.5 72
 
0.1%
16.77 24
 
< 0.1%
16.8 24
 
< 0.1%
17 360
0.3%
ValueCountFrequency (%)
49.1 24
 
< 0.1%
49 24
 
< 0.1%
48 72
 
0.1%
47 120
 
0.1%
46.5 120
 
0.1%
46.2 24
 
< 0.1%
46.06 24
 
< 0.1%
46 312
0.2%
45.89 24
 
< 0.1%
45.8 48
 
< 0.1%

tmax-obr
Real number (ℝ)

HIGH CORRELATION 

Distinct862
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.417407
Minimum12
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:28.673352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile24
Q130
median35
Q339
95-th percentile43
Maximum47
Range35
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9478342
Coefficient of variation (CV)0.17281471
Kurtosis-0.67333568
Mean34.417407
Median Absolute Deviation (MAD)5
Skewness-0.3838756
Sum4634785.7
Variance35.376731
MonotonicityNot monotonic
2024-04-26T15:28:28.817520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 6672
 
5.0%
40 6672
 
5.0%
39 6360
 
4.7%
37 5832
 
4.3%
41 5784
 
4.3%
36 5304
 
3.9%
34 5088
 
3.8%
33 4920
 
3.7%
29 4776
 
3.5%
31 4680
 
3.5%
Other values (852) 78576
58.3%
ValueCountFrequency (%)
12 24
 
< 0.1%
15 24
 
< 0.1%
16 48
 
< 0.1%
17 96
0.1%
17.7 24
 
< 0.1%
18 96
0.1%
18.5 24
 
< 0.1%
18.83 24
 
< 0.1%
19 168
0.1%
19.2 24
 
< 0.1%
ValueCountFrequency (%)
47 48
 
< 0.1%
46.5 24
 
< 0.1%
46 264
 
0.2%
45.97 24
 
< 0.1%
45.5 24
 
< 0.1%
45.4 24
 
< 0.1%
45 816
0.6%
44.85 48
 
< 0.1%
44.67 24
 
< 0.1%
44.5 96
 
0.1%

tmax-lmo
Real number (ℝ)

HIGH CORRELATION 

Distinct796
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.279594
Minimum12
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:28.932212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile24
Q128.78
median33
Q336
95-th percentile39.3
Maximum45
Range33
Interquartile range (IQR)7.22

Descriptive statistics

Standard deviation4.8981224
Coefficient of variation (CV)0.15174052
Kurtosis-0.55827959
Mean32.279594
Median Absolute Deviation (MAD)3.62
Skewness-0.36384824
Sum4346899.2
Variance23.991603
MonotonicityNot monotonic
2024-04-26T15:28:29.039684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 8136
 
6.0%
36 7968
 
5.9%
35 7632
 
5.7%
37 7032
 
5.2%
33 5688
 
4.2%
32 5616
 
4.2%
30 5568
 
4.1%
38 5448
 
4.0%
31 5256
 
3.9%
29 4896
 
3.6%
Other values (786) 71424
53.0%
ValueCountFrequency (%)
12 24
 
< 0.1%
15 24
 
< 0.1%
16 24
 
< 0.1%
17 48
 
< 0.1%
18 120
0.1%
19 192
0.1%
19.08 24
 
< 0.1%
19.2 24
 
< 0.1%
19.4 24
 
< 0.1%
19.5 24
 
< 0.1%
ValueCountFrequency (%)
45 24
 
< 0.1%
44 24
 
< 0.1%
43 144
 
0.1%
42.5 48
 
< 0.1%
42 744
0.6%
41.5 48
 
< 0.1%
41 1464
1.1%
40.9 24
 
< 0.1%
40.87 24
 
< 0.1%
40.5 288
 
0.2%

tmax-cul
Real number (ℝ)

HIGH CORRELATION 

Distinct730
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.928608
Minimum17
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:29.155212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26.73
Q131
median34.4
Q337
95-th percentile40
Maximum44
Range27
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1130365
Coefficient of variation (CV)0.12122621
Kurtosis-0.17156658
Mean33.928608
Median Absolute Deviation (MAD)2.67
Skewness-0.50479324
Sum4568962.1
Variance16.917069
MonotonicityNot monotonic
2024-04-26T15:28:29.284269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 9816
 
7.3%
37 8952
 
6.6%
34 8496
 
6.3%
35 8136
 
6.0%
38 7848
 
5.8%
32 7416
 
5.5%
33 7104
 
5.3%
31 6096
 
4.5%
39 6000
 
4.5%
30 5376
 
4.0%
Other values (720) 59424
44.1%
ValueCountFrequency (%)
17 24
 
< 0.1%
19 72
 
0.1%
20 48
 
< 0.1%
20.2 24
 
< 0.1%
20.3 24
 
< 0.1%
20.5 24
 
< 0.1%
21 168
0.1%
21.5 48
 
< 0.1%
22 336
0.2%
22.1 24
 
< 0.1%
ValueCountFrequency (%)
44 24
 
< 0.1%
43 96
 
0.1%
42 792
 
0.6%
41.5 120
 
0.1%
41.01 24
 
< 0.1%
41 2064
1.5%
40.8 24
 
< 0.1%
40.68 24
 
< 0.1%
40.5 240
 
0.2%
40.33 24
 
< 0.1%

tmin-cab
Real number (ℝ)

HIGH CORRELATION 

Distinct846
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.379118
Minimum-7
Maximum33
Zeros288
Zeros (%)0.2%
Negative672
Negative (%)0.5%
Memory size1.0 MiB
2024-04-26T15:28:29.406181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-7
5-th percentile4
Q110
median16
Q323.68
95-th percentile29
Maximum33
Range40
Interquartile range (IQR)13.68

Descriptive statistics

Standard deviation7.9828617
Coefficient of variation (CV)0.48738044
Kurtosis-1.0272796
Mean16.379118
Median Absolute Deviation (MAD)7
Skewness0.021221737
Sum2205677.5
Variance63.72608
MonotonicityNot monotonic
2024-04-26T15:28:29.537898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 4368
 
3.2%
10 4296
 
3.2%
14 4200
 
3.1%
15 4176
 
3.1%
12 4104
 
3.0%
13 4056
 
3.0%
25 4056
 
3.0%
27 4032
 
3.0%
9 4008
 
3.0%
26 3768
 
2.8%
Other values (836) 93600
69.5%
ValueCountFrequency (%)
-7 24
 
< 0.1%
-6 24
 
< 0.1%
-5 48
 
< 0.1%
-3.2 24
 
< 0.1%
-3 72
0.1%
-2.2 24
 
< 0.1%
-2 120
0.1%
-1.8 24
 
< 0.1%
-1.7 24
 
< 0.1%
-1.5 24
 
< 0.1%
ValueCountFrequency (%)
33 48
 
< 0.1%
32.86 24
 
< 0.1%
32.21 24
 
< 0.1%
32 240
 
0.2%
31.52 24
 
< 0.1%
31.41 24
 
< 0.1%
31.29 24
 
< 0.1%
31.13 24
 
< 0.1%
31 648
0.5%
30.99 24
 
< 0.1%

tmin-hmo
Real number (ℝ)

HIGH CORRELATION 

Distinct898
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.376817
Minimum-3
Maximum34
Zeros48
Zeros (%)< 0.1%
Negative24
Negative (%)< 0.1%
Memory size1.0 MiB
2024-04-26T15:28:29.666464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile7
Q113
median18
Q325
95-th percentile29
Maximum34
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0755035
Coefficient of variation (CV)0.38502334
Kurtosis-1.0538284
Mean18.376817
Median Absolute Deviation (MAD)6
Skewness-0.074171439
Sum2474695.7
Variance50.06275
MonotonicityNot monotonic
2024-04-26T15:28:29.793105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 5352
 
4.0%
16 4872
 
3.6%
17 4848
 
3.6%
25 4776
 
3.5%
26 4728
 
3.5%
13 4584
 
3.4%
14 4464
 
3.3%
11 4440
 
3.3%
15 4296
 
3.2%
28 4032
 
3.0%
Other values (888) 88272
65.5%
ValueCountFrequency (%)
-3 24
 
< 0.1%
0 48
 
< 0.1%
0.46 24
 
< 0.1%
0.66 24
 
< 0.1%
0.68 24
 
< 0.1%
1 96
0.1%
1.23 24
 
< 0.1%
1.45 24
 
< 0.1%
1.5 24
 
< 0.1%
2 144
0.1%
ValueCountFrequency (%)
34 24
 
< 0.1%
33.4 24
 
< 0.1%
33.3 24
 
< 0.1%
32 168
 
0.1%
31.8 24
 
< 0.1%
31.73 24
 
< 0.1%
31.5 48
 
< 0.1%
31.07 24
 
< 0.1%
31 984
0.7%
30.9 24
 
< 0.1%

tmin-obr
Real number (ℝ)

HIGH CORRELATION 

Distinct879
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.552475
Minimum2
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:29.929826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q113
median18
Q325
95-th percentile29
Maximum33
Range31
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.7732273
Coefficient of variation (CV)0.36508483
Kurtosis-1.1646299
Mean18.552475
Median Absolute Deviation (MAD)6
Skewness0.054593195
Sum2498350.6
Variance45.876608
MonotonicityNot monotonic
2024-04-26T15:28:30.050711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 6072
 
4.5%
26 5688
 
4.2%
27 5496
 
4.1%
15 5208
 
3.9%
13 5208
 
3.9%
25 5136
 
3.8%
16 5064
 
3.8%
10 5040
 
3.7%
12 4896
 
3.6%
17 4848
 
3.6%
Other values (869) 82008
60.9%
ValueCountFrequency (%)
2 48
 
< 0.1%
2.6 24
 
< 0.1%
3 24
 
< 0.1%
3.3 24
 
< 0.1%
4 120
 
0.1%
4.04 24
 
< 0.1%
4.1 24
 
< 0.1%
4.5 48
 
< 0.1%
4.6 24
 
< 0.1%
5 456
0.3%
ValueCountFrequency (%)
33 24
 
< 0.1%
32 192
0.1%
31.96 24
 
< 0.1%
31.92 24
 
< 0.1%
31.67 24
 
< 0.1%
31.51 24
 
< 0.1%
31.41 24
 
< 0.1%
31.36 24
 
< 0.1%
31.25 24
 
< 0.1%
31.21 24
 
< 0.1%

tmin-lmo
Real number (ℝ)

HIGH CORRELATION 

Distinct839
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.836806
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:30.170668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q113.54
median18
Q325
95-th percentile28
Maximum37
Range36
Interquartile range (IQR)11.46

Descriptive statistics

Standard deviation6.1180639
Coefficient of variation (CV)0.32479306
Kurtosis-1.2261872
Mean18.836806
Median Absolute Deviation (MAD)5.59
Skewness0.0090221431
Sum2536639.7
Variance37.430706
MonotonicityNot monotonic
2024-04-26T15:28:30.286843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 7680
 
5.7%
25 6840
 
5.1%
13 5976
 
4.4%
27 5952
 
4.4%
12 5880
 
4.4%
16 5376
 
4.0%
14 5304
 
3.9%
15 5208
 
3.9%
17 5040
 
3.7%
18 4920
 
3.7%
Other values (829) 76488
56.8%
ValueCountFrequency (%)
1 24
 
< 0.1%
3 48
 
< 0.1%
4 24
 
< 0.1%
5 120
0.1%
5.2 24
 
< 0.1%
5.4 24
 
< 0.1%
6 240
0.2%
6.5 24
 
< 0.1%
6.6 24
 
< 0.1%
6.7 24
 
< 0.1%
ValueCountFrequency (%)
37 72
 
0.1%
31 24
 
< 0.1%
30 240
0.2%
29.82 24
 
< 0.1%
29.55 24
 
< 0.1%
29.5 48
 
< 0.1%
29.36 24
 
< 0.1%
29.35 24
 
< 0.1%
29.28 24
 
< 0.1%
29.2 24
 
< 0.1%

tmin-cul
Real number (ℝ)

HIGH CORRELATION 

Distinct804
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.228211
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:30.405056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q116
median20
Q325
95-th percentile28
Maximum32
Range31
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.5107476
Coefficient of variation (CV)0.27242882
Kurtosis-0.97947983
Mean20.228211
Median Absolute Deviation (MAD)5
Skewness-0.17884119
Sum2724011.8
Variance30.36834
MonotonicityNot monotonic
2024-04-26T15:28:30.523341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 7368
 
5.5%
24 7152
 
5.3%
26 6552
 
4.9%
17 6432
 
4.8%
16 5928
 
4.4%
27 5496
 
4.1%
18 5472
 
4.1%
15 5352
 
4.0%
14 4920
 
3.7%
23 4728
 
3.5%
Other values (794) 75264
55.9%
ValueCountFrequency (%)
1 48
 
< 0.1%
3 24
 
< 0.1%
5 72
 
0.1%
6 144
0.1%
7 120
 
0.1%
7.6 24
 
< 0.1%
7.7 48
 
< 0.1%
7.73 24
 
< 0.1%
8 312
0.2%
8.2 48
 
< 0.1%
ValueCountFrequency (%)
32 48
 
< 0.1%
31 288
0.2%
30.49 48
 
< 0.1%
30.45 24
 
< 0.1%
30.42 24
 
< 0.1%
30.35 24
 
< 0.1%
30.34 24
 
< 0.1%
30.31 24
 
< 0.1%
30.18 24
 
< 0.1%
30.15 24
 
< 0.1%

prec_hmo_mm
Real number (ℝ)

ZEROS 

Distinct241
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3183194
Minimum0
Maximum117
Zeros117840
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:30.640476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum117
Range117
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.7917843
Coefficient of variation (CV)5.1518505
Kurtosis84.721288
Mean1.3183194
Median Absolute Deviation (MAD)0
Skewness8.1204282
Sum177530.16
Variance46.128333
MonotonicityNot monotonic
2024-04-26T15:28:30.768916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117840
87.5%
0.01 3312
 
2.5%
1 432
 
0.3%
1.2 360
 
0.3%
0.1 312
 
0.2%
0.3 312
 
0.2%
2 288
 
0.2%
0.5 288
 
0.2%
0.2 288
 
0.2%
3 264
 
0.2%
Other values (231) 10968
 
8.1%
ValueCountFrequency (%)
0 117840
87.5%
0.01 3312
 
2.5%
0.1 312
 
0.2%
0.2 288
 
0.2%
0.3 312
 
0.2%
0.4 168
 
0.1%
0.5 288
 
0.2%
0.6 96
 
0.1%
0.7 144
 
0.1%
0.8 192
 
0.1%
ValueCountFrequency (%)
117 24
< 0.1%
115 24
< 0.1%
103.9 24
< 0.1%
92.1 24
< 0.1%
83.5 24
< 0.1%
80.5 24
< 0.1%
80 24
< 0.1%
72.5 24
< 0.1%
72 24
< 0.1%
71.8 24
< 0.1%

prec_obr_mm
Real number (ℝ)

ZEROS 

Distinct175
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1317305
Minimum0
Maximum166.8
Zeros119088
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:30.895945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum166.8
Range166.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.6121733
Coefficient of variation (CV)5.8425333
Kurtosis148.30497
Mean1.1317305
Median Absolute Deviation (MAD)0
Skewness10.275648
Sum152403.36
Variance43.720835
MonotonicityNot monotonic
2024-04-26T15:28:31.013893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 119088
88.4%
0.01 2736
 
2.0%
1 1368
 
1.0%
0.5 912
 
0.7%
2 696
 
0.5%
1.5 456
 
0.3%
3 432
 
0.3%
2.5 384
 
0.3%
5 288
 
0.2%
9 264
 
0.2%
Other values (165) 8040
 
6.0%
ValueCountFrequency (%)
0 119088
88.4%
0.01 2736
 
2.0%
0.1 168
 
0.1%
0.2 216
 
0.2%
0.3 240
 
0.2%
0.4 24
 
< 0.1%
0.5 912
 
0.7%
0.6 96
 
0.1%
0.7 24
 
< 0.1%
0.8 24
 
< 0.1%
ValueCountFrequency (%)
166.8 24
< 0.1%
104 24
< 0.1%
102 24
< 0.1%
101 24
< 0.1%
91.7 24
< 0.1%
91 24
< 0.1%
85 24
< 0.1%
81 24
< 0.1%
80.6 24
< 0.1%
74 24
< 0.1%

prec_lmo_mm
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct175
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1055266
Minimum0
Maximum368
Zeros122808
Zeros (%)91.2%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:31.132484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.7
Maximum368
Range368
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.8859825
Coefficient of variation (CV)8.0377823
Kurtosis724.53369
Mean1.1055266
Median Absolute Deviation (MAD)0
Skewness22.004275
Sum148874.64
Variance78.960686
MonotonicityNot monotonic
2024-04-26T15:28:31.259830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122808
91.2%
1 1032
 
0.8%
0.5 720
 
0.5%
0.01 624
 
0.5%
2 480
 
0.4%
5 432
 
0.3%
1.5 384
 
0.3%
0.2 312
 
0.2%
4 264
 
0.2%
3 240
 
0.2%
Other values (165) 7368
 
5.5%
ValueCountFrequency (%)
0 122808
91.2%
0.01 624
 
0.5%
0.1 168
 
0.1%
0.2 312
 
0.2%
0.3 192
 
0.1%
0.4 144
 
0.1%
0.5 720
 
0.5%
0.6 72
 
0.1%
0.7 48
 
< 0.1%
0.8 48
 
< 0.1%
ValueCountFrequency (%)
368 24
< 0.1%
277.2 24
< 0.1%
160 24
< 0.1%
102 24
< 0.1%
97.4 24
< 0.1%
96.3 24
< 0.1%
92.71 24
< 0.1%
89.8 24
< 0.1%
88.9 24
< 0.1%
82.55 24
< 0.1%

prec_cul_mm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct300
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1633007
Minimum0
Maximum258
Zeros112416
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:31.382525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12
Maximum258
Range258
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.9661485
Coefficient of variation (CV)4.6069179
Kurtosis135.75845
Mean2.1633007
Median Absolute Deviation (MAD)0
Skewness9.1892299
Sum291318.72
Variance99.324117
MonotonicityNot monotonic
2024-04-26T15:28:31.500033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112416
83.5%
0.01 1320
 
1.0%
1 1200
 
0.9%
0.5 720
 
0.5%
1.5 600
 
0.4%
0.3 576
 
0.4%
2 552
 
0.4%
2.5 432
 
0.3%
0.2 360
 
0.3%
0.1 336
 
0.2%
Other values (290) 16152
 
12.0%
ValueCountFrequency (%)
0 112416
83.5%
0.01 1320
 
1.0%
0.1 336
 
0.2%
0.2 360
 
0.3%
0.3 576
 
0.4%
0.4 240
 
0.2%
0.5 720
 
0.5%
0.6 192
 
0.1%
0.7 72
 
0.1%
0.8 312
 
0.2%
ValueCountFrequency (%)
258 24
< 0.1%
165 24
< 0.1%
155.5 24
< 0.1%
151.4 24
< 0.1%
129 24
< 0.1%
104.4 24
< 0.1%
95.7 24
< 0.1%
94 24
< 0.1%
92.9 24
< 0.1%
92.6 24
< 0.1%

lunes_festivo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
133464 
1
 
1200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 133464
99.1%
1 1200
 
0.9%

Length

2024-04-26T15:28:31.806320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:31.897477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 133464
99.1%
1 1200
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 133464
99.1%
1 1200
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 133464
99.1%
1 1200
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 133464
99.1%
1 1200
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 133464
99.1%
1 1200
 
0.9%

martes_postfestivo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
133488 
1
 
1176

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 133488
99.1%
1 1176
 
0.9%

Length

2024-04-26T15:28:31.981601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:32.059822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 133488
99.1%
1 1176
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 133488
99.1%
1 1176
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 133488
99.1%
1 1176
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 133488
99.1%
1 1176
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 133488
99.1%
1 1176
 
0.9%

semana_santa
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
132696 
1
 
1968

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 132696
98.5%
1 1968
 
1.5%

Length

2024-04-26T15:28:32.145228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:32.237763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 132696
98.5%
1 1968
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 132696
98.5%
1 1968
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 132696
98.5%
1 1968
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 132696
98.5%
1 1968
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 132696
98.5%
1 1968
 
1.5%

1_mayo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
134280 
1
 
384

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Length

2024-04-26T15:28:32.329200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:32.402731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

10_mayo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
134328 
1
 
336

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134328
99.8%
1 336
 
0.2%

Length

2024-04-26T15:28:32.495682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:32.574933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 134328
99.8%
1 336
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 134328
99.8%
1 336
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134328
99.8%
1 336
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 134328
99.8%
1 336
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134328
99.8%
1 336
 
0.2%

16_sep
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
134280 
1
 
384

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Length

2024-04-26T15:28:32.673829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:32.759504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134280
99.7%
1 384
 
0.3%

2_nov.
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
134304 
1
 
360

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134304
99.7%
1 360
 
0.3%

Length

2024-04-26T15:28:32.862938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:32.950975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 134304
99.7%
1 360
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 134304
99.7%
1 360
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134304
99.7%
1 360
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 134304
99.7%
1 360
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134304
99.7%
1 360
 
0.3%

pre-navidad_y_new_year
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
133944 
1
 
720

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 133944
99.5%
1 720
 
0.5%

Length

2024-04-26T15:28:33.041192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:33.131260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 133944
99.5%
1 720
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 133944
99.5%
1 720
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 133944
99.5%
1 720
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 133944
99.5%
1 720
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 133944
99.5%
1 720
 
0.5%

navidad_y_new_year
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
133920 
1
 
744

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Length

2024-04-26T15:28:33.232983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:33.322627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

post-navidad_y_new_year
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
0
133920 
1
 
744

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Length

2024-04-26T15:28:33.417143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T15:28:33.495724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 134664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 133920
99.4%
1 744
 
0.6%

demanda_energia
Real number (ℝ)

HIGH CORRELATION 

Distinct3953
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2461.9686
Minimum959
Maximum5399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-26T15:28:33.593049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum959
5-th percentile1399
Q11849
median2311
Q32979
95-th percentile3989
Maximum5399
Range4440
Interquartile range (IQR)1130

Descriptive statistics

Standard deviation795.61472
Coefficient of variation (CV)0.32316202
Kurtosis-0.21121532
Mean2461.9686
Median Absolute Deviation (MAD)537
Skewness0.65903147
Sum3.3153853 × 108
Variance633002.78
MonotonicityNot monotonic
2024-04-26T15:28:33.723475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1899 95
 
0.1%
1868 95
 
0.1%
2253 94
 
0.1%
2304 94
 
0.1%
2176 94
 
0.1%
1977 93
 
0.1%
2200 93
 
0.1%
1991 91
 
0.1%
2196 90
 
0.1%
2034 90
 
0.1%
Other values (3943) 133735
99.3%
ValueCountFrequency (%)
959 1
< 0.1%
966 1
< 0.1%
980 1
< 0.1%
999 1
< 0.1%
1001 1
< 0.1%
1002 1
< 0.1%
1003 2
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
ValueCountFrequency (%)
5399 1
< 0.1%
5390 1
< 0.1%
5295 1
< 0.1%
5290 1
< 0.1%
5283 1
< 0.1%
5269 1
< 0.1%
5268 1
< 0.1%
5242 1
< 0.1%
5230 1
< 0.1%
5225 1
< 0.1%

Interactions

2024-04-26T15:28:23.835157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:53.118262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:54.781505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:56.357033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:57.934336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:59.703172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:01.254286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:02.813838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:04.545628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:06.176551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:07.816144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:09.463889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:11.228703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:12.813148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:14.464416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:16.491078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:18.408457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:20.162787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:22.062567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:23.913697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:53.210809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:54.859269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:56.436195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:58.015390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:59.779458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:01.327926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:02.889510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:04.623870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:06.261672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:07.896574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:09.545689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:11.305928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:12.895463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:14.548251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:16.585445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:18.506780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:20.248897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:22.137701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:24.007864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:53.391225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:54.941802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:56.517539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:58.099393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:59.859543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:01.412025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:02.965927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:04.706551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:06.343340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:07.979094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:09.625494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:11.391316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:12.979225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:14.637513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:16.687460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:18.602339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:20.341158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:22.225726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:24.101571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:53.467091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:55.022754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:56.595680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:58.182501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:59.941010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:01.486892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:03.049244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:04.785535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:06.424402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:08.068144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:09.708552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:11.472174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:13.062688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:14.727617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:16.787096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:18.687466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:20.427280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:22.310889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:24.211358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:53.550408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:55.106369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:56.688986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:58.265161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:00.028265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-26T15:28:21.465462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:23.356173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:25.251244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:54.456507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:56.018734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:57.599265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:59.349100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:00.924400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:02.483421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:04.052474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:05.806338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:07.480681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:09.113287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:10.886160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:12.476153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:14.097429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:16.125233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:17.992535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:19.791033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:21.554896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:23.465986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:25.345428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:54.539388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:56.106179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:57.688724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:59.445518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:01.008489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:02.567493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:04.294428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:05.896924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:07.566330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:09.203586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:10.978682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:12.563204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:14.200790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:16.216340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:18.110275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:19.884232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:21.645370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:23.560183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:25.455411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:54.624383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:56.196189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:57.776002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:59.528881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:01.096571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:02.646632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:04.381069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:05.986308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:07.655248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:09.292629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:11.066981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:12.646088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:14.293343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:16.311947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:18.220532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:19.979002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:21.895511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:23.654360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:25.533986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:54.703562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:56.276531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:57.856040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:27:59.620049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:01.176637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:02.730031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:04.464529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:06.084177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:07.736731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:09.378353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:11.145621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:12.728599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:14.378660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:16.395298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:18.312047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:20.068491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:21.979253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-26T15:28:23.742581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-04-26T15:28:33.826026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
10_mayo16_sep1_mayo2_nov.aniodemanda_energiadiahoralunes_festivomartes_postfestivomesnavidad_y_new_yearpost-navidad_y_new_yearpre-navidad_y_new_yearprec_cul_mmprec_hmo_mmprec_lmo_mmprec_obr_mmsemana_santatmax-cabtmax-cultmax-hmotmax-lmotmax-obrtmin-cabtmin-cultmin-hmotmin-lmotmin-obr
10_mayo1.0000.0000.0000.000-0.001-0.004-0.0320.0000.0030.003-0.0200.0000.0000.000-0.022-0.019-0.016-0.0180.0050.0120.0340.0120.0180.0170.005-0.0020.004-0.0030.001
16_sep0.0001.0000.0000.000-0.0010.0310.0010.0000.0650.0030.0350.0010.0010.0010.0410.0420.0070.0340.0050.0320.0240.0300.0330.0300.0390.0470.0440.0480.043
1_mayo0.0000.0001.0000.000-0.002-0.015-0.0840.0000.0300.003-0.0200.0010.0010.001-0.024-0.020-0.017-0.0190.0050.0010.0140.0000.001-0.0000.000-0.010-0.007-0.013-0.002
2_nov.0.0000.0000.0001.000-0.002-0.010-0.0810.0000.0030.0030.0680.0010.0010.001-0.023-0.010-0.005-0.0070.005-0.0070.028-0.0020.0080.003-0.0130.005-0.0140.002-0.002
anio-0.001-0.001-0.002-0.0021.0000.478-0.0050.0000.0050.000-0.0440.0000.0000.000-0.021-0.0590.016-0.0360.009-0.092-0.0470.031-0.0520.027-0.120-0.041-0.0390.045-0.035
demanda_energia-0.0040.031-0.015-0.0100.4781.0000.0120.1550.0730.0570.2700.1420.1030.0830.3170.1570.1990.1800.0870.6770.6190.7270.6490.7270.6710.7200.7380.7770.747
dia-0.0320.001-0.084-0.081-0.0050.0121.0000.0000.0990.1040.0120.1370.1370.1580.0030.0130.005-0.0140.0250.0010.009-0.0090.0160.004-0.001-0.0140.007-0.0030.002
hora0.0000.0000.0000.0000.0000.1550.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
lunes_festivo0.0030.0650.0300.0030.0050.0730.0990.0001.0000.008-0.0300.0060.0060.006-0.022-0.014-0.023-0.0180.011-0.060-0.058-0.061-0.053-0.063-0.060-0.059-0.059-0.061-0.060
martes_postfestivo0.0030.0030.0030.0030.0000.0570.1040.0000.0081.000-0.0300.0060.0060.006-0.024-0.012-0.015-0.0110.011-0.061-0.056-0.066-0.058-0.070-0.066-0.073-0.064-0.063-0.070
mes-0.0200.035-0.0200.068-0.0440.2700.0120.000-0.030-0.0301.0000.1050.1050.1660.1670.0960.1230.1110.3140.2090.2610.1850.2450.2360.2630.3710.2790.3980.339
navidad_y_new_year0.0000.0010.0010.0010.0000.1420.1370.0000.0060.0060.1051.0000.0040.004-0.026-0.021-0.023-0.0270.008-0.101-0.093-0.097-0.095-0.095-0.100-0.085-0.102-0.092-0.097
post-navidad_y_new_year0.0000.0010.0010.0010.0000.1030.1370.0000.0060.0060.1050.0041.0000.004-0.027-0.028-0.023-0.0200.008-0.098-0.092-0.098-0.100-0.096-0.105-0.087-0.099-0.095-0.098
pre-navidad_y_new_year0.0000.0010.0010.0010.0000.0830.1580.0000.0060.0060.1660.0040.0041.000-0.0200.003-0.014-0.0100.008-0.097-0.088-0.095-0.090-0.096-0.088-0.078-0.095-0.089-0.091
prec_cul_mm-0.0220.041-0.024-0.023-0.0210.3170.0030.000-0.022-0.0240.167-0.026-0.027-0.0201.0000.3300.5040.3940.0180.3220.1580.2720.2400.2680.4180.3550.3980.4180.414
prec_hmo_mm-0.0190.042-0.020-0.010-0.0590.1570.0130.000-0.014-0.0120.096-0.021-0.0280.0030.3301.0000.3390.4900.0220.1630.0690.0860.1100.0750.2960.2250.2440.2670.270
prec_lmo_mm-0.0160.007-0.017-0.0050.0160.1990.0050.000-0.023-0.0150.123-0.023-0.023-0.0140.5040.3391.0000.4320.0100.1850.0500.1380.0850.1130.2670.2130.2510.2510.253
prec_obr_mm-0.0180.034-0.019-0.007-0.0360.180-0.0140.000-0.018-0.0110.111-0.027-0.020-0.0100.3940.4900.4321.0000.0160.1860.0640.1210.1000.0810.2960.2380.2600.2750.267
semana_santa0.0050.0050.0050.0050.0090.0870.0250.0000.0110.0110.3140.0080.0080.0080.0180.0220.0100.0161.000-0.020-0.025-0.015-0.017-0.025-0.043-0.068-0.037-0.068-0.052
tmax-cab0.0120.0320.001-0.007-0.0920.6770.0010.000-0.060-0.0610.209-0.101-0.098-0.0970.3220.1630.1850.186-0.0201.0000.7710.9430.8040.8900.8770.8020.8730.7950.841
tmax-cul0.0340.0240.0140.028-0.0470.6190.0090.000-0.058-0.0560.261-0.093-0.092-0.0880.1580.0690.0500.064-0.0250.7711.0000.7900.8060.8420.7410.7610.7330.7130.744
tmax-hmo0.0120.0300.000-0.0020.0310.727-0.0090.000-0.061-0.0660.185-0.097-0.098-0.0950.2720.0860.1380.121-0.0150.9430.7901.0000.8200.9260.8170.7770.8550.7780.815
tmax-lmo0.0180.0330.0010.008-0.0520.6490.0160.000-0.053-0.0580.245-0.095-0.100-0.0900.2400.1100.0850.100-0.0170.8040.8060.8201.0000.8390.7540.7230.7990.7610.754
tmax-obr0.0170.030-0.0000.0030.0270.7270.0040.000-0.063-0.0700.236-0.095-0.096-0.0960.2680.0750.1130.081-0.0250.8900.8420.9260.8391.0000.8080.8040.8250.7840.825
tmin-cab0.0050.0390.000-0.013-0.1200.671-0.0010.000-0.060-0.0660.263-0.100-0.105-0.0880.4180.2960.2670.296-0.0430.8770.7410.8170.7540.8081.0000.8590.9090.8490.909
tmin-cul-0.0020.047-0.0100.005-0.0410.720-0.0140.000-0.059-0.0730.371-0.085-0.087-0.0780.3550.2250.2130.238-0.0680.8020.7610.7770.7230.8040.8591.0000.8490.8870.916
tmin-hmo0.0040.044-0.007-0.014-0.0390.7380.0070.000-0.059-0.0640.279-0.102-0.099-0.0950.3980.2440.2510.260-0.0370.8730.7330.8550.7990.8250.9090.8491.0000.8850.926
tmin-lmo-0.0030.048-0.0130.0020.0450.777-0.0030.000-0.061-0.0630.398-0.092-0.095-0.0890.4180.2670.2510.275-0.0680.7950.7130.7780.7610.7840.8490.8870.8851.0000.918
tmin-obr0.0010.043-0.002-0.002-0.0350.7470.0020.000-0.060-0.0700.339-0.097-0.098-0.0910.4140.2700.2530.267-0.0520.8410.7440.8150.7540.8250.9090.9160.9260.9181.000

Missing values

2024-04-26T15:28:25.688504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-26T15:28:26.141312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

horadiamesaniotmax-cabtmax-hmotmax-obrtmax-lmotmax-cultmin-cabtmin-hmotmin-obrtmin-lmotmin-culprec_hmo_mmprec_obr_mmprec_lmo_mmprec_cul_mmlunes_festivomartes_postfestivosemana_santa1_mayo10_mayo16_sep2_nov.pre-navidad_y_new_yearnavidad_y_new_yearpost-navidad_y_new_yeardemanda_energia
0011200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101394
1111200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101297
2211200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101255
3311200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101222
4411200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101168
5511200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101128
6611200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101100
7711200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101083
8811200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101076
9911200721.022.025.030.029.02.09.08.010.09.00.00.00.00.000000000101022
horadiamesaniotmax-cabtmax-hmotmax-obrtmax-lmotmax-cultmin-cabtmin-hmotmin-obrtmin-lmotmin-culprec_hmo_mmprec_obr_mmprec_lmo_mmprec_cul_mmlunes_festivomartes_postfestivosemana_santa1_mayo10_mayo16_sep2_nov.pre-navidad_y_new_yearnavidad_y_new_yearpost-navidad_y_new_yeardemanda_energia
13465414125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000003601
13465515125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000003844
13465616125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000003971
13465717125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000004040
13465818125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000004014
13465919125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000003861
13466020125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000003668
13466121125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000003692
13466222125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000003777
13466323125202234.037.038.036.040.015.020.019.022.021.00.00.00.00.000000000003796